A common problem in high energy physics is the desire to use state-of-the art machine learning methods to solve real-world problems encountered in physics experiments.
Often, the leading edge of machine learning is driven by a standard suite of problems which are motivated by the practical interests of tech industries.
For example, convolutional neural networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for natural language processing are some of the most well-studied and developed areas in deep learning.
In an effort to reap the benefits of these advances, physicists have been systematically studying the efficacy of these existing methods when applied to typical problems in our field such triggering, event reconstruction, and object tagging.[1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}
